Writer Identification: Deep Learning with ResNet50 for Offline Urdu Handwritten Documents

The field of writer identification, a crucial aspect of document analysis, has undergone a significant evolution through the incorporation of deep learning models. This paper presents an innovative writer identification system, meticulously designed around the ResNet50 deep learning architecture, with a specific emphasis on its adaptability for processing offline Urdu handwritten documents. The system underwent rigorous training and thorough testing using an Urdu handwritten dataset that encompasses contributions from 418 Urdu writers. Our ResNet50-based model demonstrates an exceptional level of accuracy, achieving an impressive identification rate of 99.26%. Within this paper, we delve into a comprehensive examination of the model’s architecture, intricate feature extraction methods, and the subtle intricacies of its training methodologies, all of which collectively contribute to this outstanding performance. Beyond pushing the boundaries of current writer identification capabilities, the application of deep learning, particularly the ResNet 50 model, to Urdu handwritten documents holds significant promise across various domains, including historical document preservation, forensic analysis, and robust authentication. This paper not only introduces a state-of-the-art writer identification system but also underscores the broader impact of deep learning in the field of document analysis. The remarkable accuracy demonstrated by our ResNet50-based model highlights the transformative potential of deep learning, enhancing the precision and effectiveness of writer identification tasks. Furthermore, it illuminates new avenues for research and practical applications, spanning diverse linguistic and cultural contexts.